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Record W7135174806 · doi:10.2298/fil2521403l

Theoretical analysis on the nonlinear fractional differential equations and generalized heat equation

2025· article· en· W7135174806 on OpenAlexafffund
C. K. Li, Ehsan Pourhadi

Bibliographic record

VenueFilomat · 2025
Typearticle
Languageen
FieldMathematics
TopicNonlinear Differential Equations Analysis
Canadian institutionsBrandon University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUniquenessHeat equationNonlinear systemBoundary value problemOperator (biology)InverseInverse problemPartial differential equation

Abstract

fetched live from OpenAlex

Using Schauder's fixed-point theorem, we establish sufficient conditions for the existence and uniqueness of solutions to the nonlinear fractional boundary value problem: \begin{cases} {}_{c}D^{\beta}\zeta(x) + f(x, \zeta(x), I^{\gamma}\zeta(x)) = 0, & x \in I = [0, 1], \quad 1 0, \\?(0) = 0, \quad \zeta(1) = \phi(\zeta), \end{cases} {(0.1)} where \phi is a functional defined on C(I, \mathbb{R}) . By constructing an appropriate Green''s function, we derive a Lyapunov-type inequality for a special case of the problem (0.1): \begin{cases} {}_{c}D^{\beta}\zeta(x) + \lambda(x)I^{\gamma}\zeta(x) = \eta(x, \zeta(x)), & x \in I = [0, 1], \quad 1 0, \\?(0) = 0, \quad \zeta(1) = \phi(\zeta). \end{cases} {(0.2)} We further make an analysis for equation (0.2) by applying the inverse operator method and the Mittag-Leffler function with illustrative examples demonstrating applications obtained. Finally, we construct an analytic solution to the following generalized fractional heat equation with an initial condition in n dimensions based on an inverse operator: \begin{cases} {}_{c}D_{t}^{\alpha}u(t, x) = \Delta_{a_{1}(x_{1}), \cdots, a_{n}(x_{n})}u(t, x) + f(t, x), & (t, x) \in \mathbb{R}^{+} \times \mathbb{R}^{n}, \quad 0 < \alpha \leq 1, \\u(0, x) = \psi(x), \end{cases} {(0.3)} where \Delta_{a_{1}(x_{1}), \cdots, a_{n}(x_{n})} = a_{1}(x_{1})\frac{\partial^{2}}{\partial x_{1}^{2}} + \cdots + a_{n}(x_{n})\frac{\partial^{2}}{\partial x_{n}^{2}}.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.327
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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